GEN AI Engineer

This is a hands-on engineering role. You will design and ship agentic systems in Python, build retrieval-augmented applications that run in production under real load, and turn what works into reusable accelerators, templates, and standards that other engineering teams adopt. You will also act as the subject matter expert for agentic and generative AI within the engineering organization, guiding and mentoring teams as they take these practices on. The strongest fit is an engineer, not a modeller. We are looking for someone whose foundation is software development backend, full-stack, or platform engineering who moved into AI over

the last two years and has since shipped RAG applications into production and agents used by real users. Machine learning engineers with genuine production deployment experience are equally welcome. This is not a data science role: there is no model training, no feature engineering, and no experimentation track, so profiles built purely on notebooks, modelling, or prompt-only work will not map to the day-to-day.

Responsibilities:

• Build agentic systems design and implement agents and autonomous workflows that run in production, including tool integration, failure handling, termination conditions, and cost control.
• Ship with AI-assisted development platforms to deliver production-quality software using AI-assisted development tooling and set the practice for how it is used well.
• Establish standards, establish, maintain, and evolve standards, patterns, governance frameworks, and best practices for AI-assisted software engineering.
• Evaluate emerging tooling, assess new AI development tools, frameworks, and platforms, and recommend an enterprise adoption strategy.
• Build reusable assets, develop accelerators, templates, prompt libraries, and enablement assets that improve engineering productivity and consistency.
• Act as subject matter expert; serve as the SME for agentic development, generative AI, and AI-powered software delivery, providing guidance and mentorship across teams.

Requirements:

• Python; strong production proficiency.
• TypeScript/JavaScript or Java accepted as a secondary language where your AI work was built in them.
• Retrieval-Augmented Generation (RAG): at least one RAG application you took to production and operated, not a prototype.
• Agentic AI: hands-on design and implementation of agents and autonomous workflows with real users.
• You should be able to walk through tool integration, what failed, and how you bounded it.
• Prompt engineering: applied prompt and context work as part of shipping systems.
• Agent frameworks: working experience with any one of LangGraph, LangChain, AutoGen, CrewAI, or Semantic Kernel.
• AI-assisted development platforms' daily production use of Claude Code, GitHub Copilot, Cursor, Windsurf, or equivalent.
• Engineering background: 5+ years total in software engineering, with roughly the last two years in the AI space.
• Driving adoption ability to take a practice across multiple teams, with evidence that teams actually adopted what you built.

Nice-to-Have / Preferred:

• Reusable engineering assets: prompt libraries, templates, playbooks, or accelerators adopted broadly across an organization. AI governance frameworks, onboarding guides, training materials, or operational processes for AI-assisted software engineering.
• Model Context Protocol (MCP) familiarity with MCP and enterprise orchestration patterns.
• Multi-agent architecture experience coordinating multiple agents. Autonomous coding agents' exposure to Devin or equivalent.
• Financial services exposure: A BFSI or capital markets background is advantageous.

Annexure Skills and Requirements:

• Depth Ownership: You have led this and can defend the decisions under deep questioning.
• Hands-on: You have built it directly and can explain your part.
• Group Skill / Framework / Tool Required: Depth Group 1 Programming Python Ownership Group 2 Generative AI Engineering Retrieval-Augmented Generation (RAG) Ownership Agentic AI Ownership Prompt engineering.
• Hands-on Group 3 Agent Framework: LangGraph, LangChain, AutoGen, CrewAI, or Semantic Kernel (any one).